local discovery algorithms

aracne

chow.liu

ARACNE and Chow-Liu learn simple graphs structures from data using pairwise
 mutual information coefficients.

local learning

Bayesian network structure learning, parameter learning and inference.
This package implements constraint-based (PC, GS, IAMB, Inter-IAMB, Fast-IAMB, MMPC,
Hiton-PC, HPC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing and Tabu
Search) and hybrid (MMHC, RSMAX2, H2PC) structure learning algorithms for discrete,
Gaussian and conditional Gaussian networks, along with many score functions and
conditional independence tests.
The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are also implemented.
Some utility functions (model comparison and manipulation, random data generation, arc
orientation testing, simple and advanced plots) are included, as well as support for
parameter estimation (maximum likelihood and Bayesian) and inference, conditional
probability queries, cross-validation, bootstrap and model averaging.
Development snapshots with the latest bugfixes are available from <https://www.bnlearn.com/>.

Marco Scutari

bnlearn

Bayesian Network Structure Learning, Parameter Learning and
Inference

local discovery algorithms function

<dl> <dt>x</dt>
<dd>a data frame containing the variables in the model.</dd> <dt>whitelist</dt>
<dd>a data frame with two columns (optionally labeled "from"
 and "to"), containing a set of arcs to be included in the graph.</dd> <dt>blacklist</dt>
<dd>a data frame with two columns (optionally labeled "from"
 and "to"), containing a set of arcs not to be included in the graph.</dd> <dt>mi</dt>
<dd>a character string, the estimator used for the pairwise (i.e.
 unconditional) mutual information coefficients in the ARACNE and Chow-Liu
 algorithms. Possible values are <code>mi</code> (discrete mutual information)
 and <code>mi-g</code> (Gaussian mutual information).</dd> <dt>debug</dt>
<dd>a boolean value. If <code>TRUE</code> a lot of debugging output is
 printed; otherwise the function is completely silent.</dd></dl>

Arguments

Author

ARACNE and Chow-Liu learn simple graphs structures from data using pairwise
 mutual information coefficients.

Local discovery structure learning algorithms — local discovery algorithms

<dl>

 <dt>x</dt>
<dd>a data frame containing the variables in the model.</dd>

 <dt>whitelist</dt>
<dd>a data frame with two columns (optionally labeled "from"
 and "to"), containing a set of arcs to be included in the graph.</dd>

 <dt>blacklist</dt>
<dd>a data frame with two columns (optionally labeled "from"
 and "to"), containing a set of arcs not to be included in the graph.</dd>

 <dt>mi</dt>
<dd>a character string, the estimator used for the pairwise (i.e.
 unconditional) mutual information coefficients in the ARACNE and Chow-Liu
 algorithms. Possible values are <code>mi</code> (discrete mutual information)
 and <code>mi-g</code> (Gaussian mutual information).</dd>

 <dt>debug</dt>
<dd>a boolean value. If <code>TRUE</code> a lot of debugging output is
 printed; otherwise the function is completely silent.</dd>

</dl>

local discovery algorithms: Local discovery structure learning algorithms

Description

Usage

Value

Arguments

Author

See Also